Courses with Gaps
BRIDGE's partner institutions have flagged these courses as needing diaspora expertise. Browse the list below and express interest in teaching a course to start the conversation with the institution.
240
Courses with Gaps
10
Faculties
168
Programmes
Faculty: Computing ×
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Showing 21–24
of 24 courses
MTH 101
2
1 institution need this
At the end of the course students should be able to: 1. understand the basic definition of Set, Subset, Union, Intersection, Complements and use of Venn diagrams; 2. solve quadratic equations; 3. solve trigonometric func...
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Elementary set theory, subsets, union, intersection, complements, venn diagrams. Real
numbers; integers, rational and irrational numbers, mathematical induction, real sequences
and series, theory of quadratic equations, binomial theorem. Complex numbers; algebra of
complex numbers; the Argand diagram. De-Moivre’s theorem, nth roots of unity. Circular
measure, trigonometric functions of angles of any magnitude, addition and factor formulae.
CSC 402
2
1 institution need this
At the end of the course, students should be able to: 1. state laws and regulations related to ethics; 2. identify and explain relevant codes of ethics for computing practice; 3. identify social and ethical issues in dif...
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Addresses social, ethical, legal and managerial issues in the application of Computer Science
to the information technology industry. Through seminars and case studies, human issues
confronting Computer Science graduates will be addressed. Topics include managerial and
personal ethics, computer security, privacy, software reliability, personal responsibility for the
quality of work, intellectual property, environment and health concerns, and fairness in the
workplace.
SEN 401
2
1 institution need this
At the end of this course, students should be able to: 1. state the importance of software configuration management; 2. explain the typical processes in software configuration management; and 3. describe the key issues i...
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Management of the software configuration management process – organisation context for
software configuration management, constraints and guidance for software configuration
management process. Planning for software configuration management, software
configuration management plan, and surveillance of software configuration management.
Software configuration identification and software library. Software configuration control –
requesting, evaluating and approving software changes, implementing software changes, and
deviations and waivers. Software configuration status accounting – software configuration
status information and reporting. Software configuration auditing. Key issues in software
maintenance – technical issues, management issues, maintenance cost estimation, and
software maintenance measurement. Maintenance process – maintenance processes and
activities. Techniques for maintenance – program comprehension, re-engineering, reverse
engineering, migration, and retirement.
Lab Work: Practical demonstration of software configuration management processes.
Working with software configuration management software. Illustration of software
maintenance processes and activities. Working with software maintenance software.
Illustration of software re-engineering and reverse engineering techniques.
DTS 204
3
1 institution need this
At the end of the course, the students should be able to: 1. make conclusions based on statistical assumptions, models and results; 2. make inference on statistical outcomes, and real-world implications and how these out...
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Population and samples. Asymptotics. Statistical models and methodologies. Random
sampling distributions. Elementary time series analysis. Index numbers. Demographic
measures. Estimation (point and interval) and tests of hypotheses concerning population
mean and proportion (one and two sample cases). Regression and correlation. Programming
in Python computer language. Computation of mean, variance and correlation. Sorting and
ranking of data. Data Step Processing. Preparing Data for Analysis. Evaluating Quantitative
Data. Sample Size Estimation. Basic statistical computing in regression analysis and the
analysis of designed experiments. Introduction to Monte Carlo methods. Use of statistical
packages like SPSS, SAS, Minitab, GENSTAT, EPI-INFO, SYSTAT.
Lab work: Practical experiments on statistical models and methodologies. Practical exercises
on random sampling distribution methods. Practicals on test of hypothesis, population, mean,
proportion, regression and correlation analysis. Exercise on how to sort and data from different
data set. Use of SPSS for data analysis and computation.